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Rosemary M. Dyer

Abstract

During the winters of 1961–62 and 1963–64, measurements of light scattering by falling snow provided a continuous record of snowfall rates on the campus of McGill University in Montreal. This permitted an analysis of the time variations of intensity during a snowstorm, with a resolution not possible when a heated tipping bucket is the measuring device.

It was found in the course of this study that a major portion of the variance in snowfall rate measured at the ground during a single storm can be explained in terms of a simple Markov process, in which the snowfall rate at each time interval appears to be affected by its value one time step before, but is independent of the rate more than one time step before. Under certain circumstances, this may provide a method of predicting total snowfall accumulations.

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ROSEMARY M. DYER

Abstract

A mathematical filter for eliminating persistence in meteorological data is proposed and discussed. This filter takes the form Zt=Xt−ρ1Xt−l. Relationships between statistical parameters of the filtered and the original data are derived and found to depend only on the value of ρl. Examples of the effect of the filter on the power spectrum of various types of input data are also given.

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Rosemary M. Dyer and Ian D. Cohen

Abstract

The results of a spectral analysis of the horizontal fluctuations in temperature and liquid water content measured by instrumented aircraft at four altitudes daily during four days in the life-cycle of a storm moving eastward across the United States are discussed, along with a synoptic analysis of the storm. The storm itself was typical of the large scale systems travelling across the continent during the winter season.

Each stage of the storm (development, maturity and dissipation) exhibited distinctive spectral characteristics. In addition, the evidence that the age and previous history of the system greatly affect the fluctuation spectra is strong.

The results of this study support the thesis of a characteristic spectral signature for cloud systems, as well as for single clouds. This method may be used to determine the present growth stage of a storm system, and its potential for future development.

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Herbert E. Hunter, Rosemary M. Dyer, and Morton Glass

Abstract

Classification algorithms have been developed to distinguish six categories of cloud ice particles. These algorithms have been incorporated in schema which, when applied to shadowgraph images produced by the Precision Measurement System laser scanning device, have demonstrated the capability of classifying with more consistency than human classifiers, and with almost no sensitivity to particle orientation.

The data used to derive the algorithms consisted of observations obtained on four separate aircraft flights. Two human classifiers, interacting with a preliminary machine classification, defined the correct answers for this training data set. The algorithms were then tested against arbitrarily selected segments from two additional flights. The ADAPT Service Corporations eigenvector, or empirical orthogonal function (EOF) technique, defined the features objectively, and the ADAPT independent eigenscreening algorithm development program related these features to the particle type.

Analysis of the performance suggests that considerable variation is to be expected, based on the set-to-set variation of the distribution of particle types between real data sets. The classification schema have been developed to allow the user to change key parameters in order to compensate for this variation.

It was concluded that the machine classification was superior to manual classification for the identification of large numbers of particles in terms of speed and consistency.

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Rosemary M. Dyer, Morton Glass, and Herbert E. Hunter

Abstract

A major impediment to the development of computer algorithms for the automatic classification of ice particle types found in the atmosphere as measured by a Particle Measuring System two-dimensional probe is the difficulty of obtaining training data. This is especially true when, as is usually the case, the particle shapes do not correspond to any of the pure crystal types found in textbooks.

This paper presents the results of testing such a training set. Sources of bias among human observers include the effect of training and previous familiarity with the data, fatigue, and particle orientation, as well as subjective differences among observers. The deviation of individual human observers from the classifications arrived at by consensus indicates an upper bound to the accuracy possible in automated classification schemes.

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